论文标题
基于视频的子米本地化在资源受限平台上的可行性
Feasibility of Video-based Sub-meter Localization on Resource-constrained Platforms
论文作者
论文摘要
尽管基于卫星的全球定位系统（GPS）足以适合某些户外应用，但许多其他应用程序被其多米定位错误和室内覆盖范围较差所阻止。在本文中，我们研究了在资源受限平台上基于实时视频的本地化的可行性。在开始本地化任务之前，基于视频的本地化系统下载了一个限制目标环境的离线模型，例如一组城市街道或室内购物中心。然后，系统能够将用户本地定位在模型中，仅使用视频作为输入。 To enable such a system to run on resource-constrained embedded systems or smartphones, we (a) propose techniques for efficiently building a 3D model of a surveyed path, through frame selection and efficient feature matching, (b) substantially reduce model size by multiple compression techniques, without sacrificing localization accuracy, (c) propose efficient and concurrent techniques for feature extraction and matching to enable online localization, (d) propose a method with交错的功能匹配和基于光流的跟踪，以减少在线本地化中的特征提取和匹配时间。 基于用位置地面真相手动注释的一系列室内和室外视频，我们证明，尽管视频条件有挑战，但在智能手机类型平台上可以实现次级准确性。
While the satellite-based Global Positioning System (GPS) is adequate for some outdoor applications, many other applications are held back by its multi-meter positioning errors and poor indoor coverage. In this paper, we study the feasibility of real-time video-based localization on resource-constrained platforms. Before commencing a localization task, a video-based localization system downloads an offline model of a restricted target environment, such as a set of city streets, or an indoor shopping mall. The system is then able to localize the user within the model, using only video as input. To enable such a system to run on resource-constrained embedded systems or smartphones, we (a) propose techniques for efficiently building a 3D model of a surveyed path, through frame selection and efficient feature matching, (b) substantially reduce model size by multiple compression techniques, without sacrificing localization accuracy, (c) propose efficient and concurrent techniques for feature extraction and matching to enable online localization, (d) propose a method with interleaved feature matching and optical flow based tracking to reduce the feature extraction and matching time in online localization. Based on an extensive set of both indoor and outdoor videos, manually annotated with location ground truth, we demonstrate that sub-meter accuracy, at real-time rates, is achievable on smart-phone type platforms, despite challenging video conditions.
